1. Field of the Invention
Embodiments of the invention provide techniques for computationally analyzing a sequence of video frames. More specifically, embodiments of the invention relate to techniques for detecting a foreground object in a scene depicted in the sequence of video frames.
2. Description of the Related Art
Some currently available video surveillance systems provide simple object recognition capabilities. For example, a video surveillance system may be configured to classify a group of pixels (referred to as a “foreground object”) in a given frame as being a particular object (e.g., a person or vehicle). Once identified, a foreground object may be tracked from frame-to-frame in order to follow the foreground object moving through the scene over time, e.g., a person walking across the field of vision of a video surveillance camera.
However, such surveillance systems typically rely on a background model to extract foreground object(s) from the scene. The foreground object(s) that are extracted from the background model may be spurious and/or unreliable depending on characteristics of the camera or the particular environment. Tracking spurious foreground object(s) is undesirable. To further complicate things, the background model may return fragmented foreground objects in a very complex environment, presenting an additional challenge to the tracker. In order to for any surveillance system to identify objects, events, behaviors, or patterns as being “normal” or “abnormal” the foreground objects should be correctly detected and tracked. Accordingly, what is needed is accurate foreground object detection and tracking that produces reliable results in real-time.